• DocumentCode
    240359
  • Title

    The evident use of evidence theory in big data analytics using cloud computing

  • Author

    Mcheick, Hamid ; Mohammad, Atif Farid

  • Author_Institution
    Comput. Sci. Dept., Univ. of Quebec at Chicoutimi, Chicoutimi, QC, Canada
  • fYear
    2014
  • fDate
    4-7 May 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    We live in the world of evidence. This research survey comprises of several research works and has an example implying dempster-shafer theory of evidence. We have witnessed several advances in computational performance, which have brought us the design and development of high-performance computing simulation tools. It is a fact that we have to account for uncertainty, while generating such high-performance systems using such simulation tools can fail in service performance predictions. We have seen that evidence theory is utilized to measure uncertainty in terms of the uncertain measures of belief and plausibility. It is also witnessed in computing community that Cloud computing has provided a flexible and scalable infrastructures to grow beyond contemporary borders to the organizations as wells the users everyday use of services. It also has increased availability of high-performance computing applications to small/ medium-sized businesses as well as academic users to work with. This paper also sheds light on Cloud computing and Service-Oriented Architecture.
  • Keywords
    Big Data; cloud computing; data analysis; inference mechanisms; service-oriented architecture; uncertainty handling; Big Data analytics; Dempster-Shafer theory; cloud computing; evidence theory; service-oriented architecture; uncertainty measure; Abstracts; Computational modeling; Data models; Forgery; Personnel; Cloud Computing; Evidence Theory; High-Performance Computing; Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference on
  • Conference_Location
    Toronto, ON
  • ISSN
    0840-7789
  • Print_ISBN
    978-1-4799-3099-9
  • Type

    conf

  • DOI
    10.1109/CCECE.2014.6901158
  • Filename
    6901158